The present invention relates to respiratory monitoring and, more particularly, to methods and systems for estimating a subject's respiratory airflow from a body sound signal detected by an acoustic sensor on the subject's body.
Respiratory airflow data, such as current respiratory airflow rate data, peak expiratory flow rate (PEFR) data and forced expiratory volume in the first second (FEV1) data, can be used to diagnose and treat respiratory ailments, such as asthma and chronic obstructive pulmonary disease (COPD), and to monitor the respiratory health of people who suffer from respiratory ailments. Such data can also be useful in other contexts, such as in detecting airborne hazards in occupational settings.
Spirometry is considered the gold standard for accurate and repeatable measurement of respiratory airflow. However, spirometry is not well-adapted for monitoring of a human subject in the field as he or she goes about his or her daily activities, or even for at-home monitoring. Spirometry requires access to a spirometer having a mouthpiece attached to a tube, which is in turn attached to electronic sensors, which are in turn attached to a computer that measures respiratory airflow parameters. Moreover, to get an accurate reading from a spirometer, a subject must often wear nose clips to ensure that breathing is done through the mouth, and must sit up straight, with feet against the floor, and with head facing forward.
Recent attempts have been made to estimate respiratory airflow from a body sound signal detected by an on-body acoustic sensor, which can be worn by a subject as he or she performs a daily routine, and also at home. Some of these attempts have taken advantage of a general correlation between body sound signal amplitude or entropy and respiratory airflow. In one approach, a body sound signal and respiratory airflow are detected by an acoustic sensor mounted at a subject's trachea and a spirometer, respectively, over a common time frame. Time-aligned signal entropy and respiratory airflow data points are fed into an analytical model that produces a best fit equation describing the relationship between signal entropy and respiratory airflow across the entire spectrum of measured entropies and airflows. Subsequent body sound signal readings (e.g. taken in the field or at home) are thereafter plugged-in to the best fit equation to yield estimates of respiratory airflow, without any further need for a spirometer.
Unfortunately, known approaches to estimating respiratory airflow from a body sound signal detected by an on-body acoustic sensor have so far met with only limited success. No best fit equation describing the relationship between a characteristic of a body sound signal and respiratory airflow has proven accurate for the general population, or across an entire spectrum of body sound signals and respiratory airflows experienced by a given subject. For example, a best fit equation that is accurate for a given subject at low airflows will be inaccurate for the subject at high airflows, or vice versa.